Identification of Anti-virulence Compounds That Disrupt Quorum-Sensing Regulated Acute and Persistent Pathogenicity
Bibliographic record
Abstract
Etiological agents of acute, persistent, or relapsing clinical infections are often refractory to antibiotics due to multidrug resistance and/or antibiotic tolerance. Pseudomonas aeruginosa is an opportunistic Gram-negative bacterial pathogen that causes recalcitrant and severe acute chronic and persistent human infections. Here, we target the MvfR-regulated P. aeruginosa quorum sensing (QS) virulence pathway to isolate robust molecules that specifically inhibit infection without affecting bacterial growth or viability to mitigate selective resistance. Using a whole-cell high-throughput screen (HTS) and structure-activity relationship (SAR) analysis, we identify compounds that block the synthesis of both pro-persistence and pro-acute MvfR-dependent signaling molecules. These compounds, which share a benzamide-benzimidazole backbone and are unrelated to previous MvfR-regulon inhibitors, bind the global virulence QS transcriptional regulator, MvfR (PqsR); inhibit the MvfR regulon in multi-drug resistant isolates; are active against P. aeruginosa acute and persistent murine infections; and do not perturb bacterial growth. In addition, they are the first compounds identified to reduce the formation of antibiotic-tolerant persister cells. As such, these molecules provide for the development of next-generation clinical therapeutics to more effectively treat refractory and deleterious bacterial-human infections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".